# RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm

arXiv: https://arxiv.org/abs/2411.08392 · HTML: https://mlsanity.com/paper/2411.08392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 5 of 5)

- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Reinforcement Learning for Economic Policy: A New Frontier?](https://mlsanity.com/paper/2206.08781/) (2022)
- [Reinforcement Learning Applications](https://mlsanity.com/paper/1908.06973/) (2019)

## Abstract (arXiv)

Reinforcement Learning (RL) is a rapidly growing area of machine learning that finds its application in a broad range of domains, from finance and healthcare to robotics and gaming. Compared to other machine learning techniques, RL agents learn from their own experiences using trial and error, and improve their performance over time. However, assessing RL models can be challenging, which makes it difficult to interpret their behaviour. While reward is a widely used metric to evaluate RL models, it may not always provide an accurate measure of training performance. In some cases, the reward may seem increasing while the model's performance is actually decreasing, leading to misleading conclusions about the effectiveness of the training. To overcome this limitation, we have developed RLInspect - an interactive visual analytic tool, that takes into account different components of the RL model - state, action, agent architecture and reward, and provides a more comprehensive view of the RL training. By using RLInspect, users can gain insights into the model's behaviour, identify issues during training, and potentially correct them effectively, leading to a more robust and reliable RL system.